In many areas of copier/printer/scanner image quality testing, it is desirable to start with a known test or master, process it through the machine under test, and analyze the resulting image. Based on the results of this analysis, the machine under test, can be adjusted, calibrated, or compensated via various control points. For example, if a halftone pattern is being reproduced on a laser printer, the resulting reflectance of the electronically generated halftone can change from printer to printer or overtime for the same printer. If the output from a printer is digitized via a scanner, the printer's response to an applied halftone can be measured and compensated for by modifying parameters within the halftoning process such as the tonal reproduction curve. Thus, by applying a known input, and measuring the error between the desired output and the actual output, a matrix of correction terms can be derived to obtain the desired output from the machine.
FIG. 1 illustrates a conventional system used to calibrate a monochrome printer. A scanner 1 scans in a master or target image having a predetermined set of test patches. This master image is stored in a master image memory 10. When calibrating the printer, the stored master image is fed to a printer 9 via a compensating circuit 7 which allows the master image to pass therethrough without processing. The printer 9 prints the master image on a recording medium which is fed back into the scanner 1. The scanned in image is fed to an analyzer 3 which compares the image data values of the scanned in image with the master image data values fed from the master image memory 10. The analyzer 3 determines the errors or differences between the two images and produces calibration values therefrom which are stored as a new screen matix in a calibration values memory 5. The calibration values are used by compensating circuit 7 to correct image data subsequently sent to the printer 9 so that the image is reproduced accurately.
The calibration technique described with respect to FIG. 1 can also be extended to calibrate color printers. However, due to the increase number of output attributes, calibration in the color domain is more complex. In addition, the calibration technique can be extended to line width/growth image quality diagnostics, photorecpetor deletions, etc.
First, the color scanner itself needs to be properly calibrated since most scanners are not colorimetric. The conventional scanner calibration is done by scanning a color test pattern with the scanner. The scanner R, G, B readings are then correlated with the CIE/XYZ values of the patches measured with a colorimeter. Grey patches in the test pattern can be used to establish the relationship between the scanner R, G, and B values and the luminance intensity L. The L equivalent scanned R, G, and B values are then multiplied by a 3.times.3 matrix to yield the X, Y, and Z values. The matrix of correction coefficients are determined by regression analysis to minimize the difference between the measured and calculated X, Y, and Z values. With the scanner calibrated, the device dependent scanner R, G, and B values can then be related to the device independent standard measures, such as the CIE/XYZ values.
Once the scanner is calibrated, the printer is calibrated. There are several methods of performing color printer output calibrations, such calibrations can be classified as algorithmic, table look-up, or a hybrid approach.
In calibrating the color printer, conventionally, a printed test image is scanned by a calibrated scanner. A resulting 24 bit image in LAB space (30 bits in RGB space) is then analyzed (ten bits per RGB color scan), and the average scan patch RGB values are determined and converted to device independent data. The device independent data can then be processed to convert the data into L*C*h* space. Interpolated RGB levels corresponding to a minimum chroma can then be the basis of a set of grey balance screens or a set of new seed RGB values for a new test pattern generation, if further iteration is required.
The second step in the conventional color calibration is to determine the color correction matrix or matrices that will enable a match between the input and output colors. Initially, color seed data is utilized to print multiple 3.times.9 matrices of color patches. The colors in each matrix correspond to all the possible combinations of increasing and decreasing RGB values by a fixed amount around a center value targeted towards a selected set of colors. The pattern is then printed on the color printer to be calibrated.
The printed patterns are then scanned with the scanner RGB values of the patches being converted to the CIE/L*a*b* and the color difference between the printed patches and the corresponding test target are computed. The RGB values of the patch with the minimum color difference are then used as color seed data in the next iteration. Upon obtaining a set of modified RGB values that have a small enough color difference, a multiple linear regression analysis is performed to determine the matrix needed to transform the input RGB to the modified RGB. One way of conventionally transforming the input to the modified RGB is utilizing a process which weights each term by the sum of the squares of partial differentials of L*, a*, and b* with respect to R, G, and B values evaluated at the target RGB points. Once the transformation is established between the input RGB to the modified RGB, these values can be utilized to calibrate the color printer.
As discussed above, advances in digital image processing make it possible to scan, digitize, and analyze xerographic prints for image quality defects for printing/scanning calibration purposes. However, one of the difficulties in performing this task automatically is the lack of a fast, accurate method for locating the coordinates and scale of the scanned image relative to the original paper document. In other words, conventional calibration systems have a problem establishing a proper positional relationship between the scanned in image and the target or master image which was utilized to generate the original target document.
More specifically, if the printing of a color or monochrome patch is slightly skewed or offset from its original target or master position or the scanned in color or monochrome test patch is skewed or offset from its actual printed position, the positional deviation will cause problems in the calibration system. It is not always possible to scan the page from the printer so that the position of the pixel (Xp, Yp) of the master image exactly corresponds to the position of the pixel (Xs, Ys) of the scanned image. Thus, a system which determines a coordinate transformation between a master image and a scanned image can counteract the problems associated with offset, scale, and rotation.
For example, if a test patch is offset by 20 pixels from its original targeted position, the calibration analysis, expecting a color, monochrome, or grey patch in a certain 20 pixel wide region, will produce a false calibration by recording an error in this area since no patch was detected because the expected patch was not printed within the certain 20 pixel wide region due to the 20 pixel offset. Any calibration values resulting from this false calibration do not reflect the actual master test print and the scanned data since the error was due to positional deviation.
To overcome this problem in a calibration system, a target locater system is utilized to inform the scanner of the actual position of the printed test patches so that the image data read by the scanner can be properly transformed positionally to enable comparison with the correct reference data.
Conventionally, transformation systems have used location systems that depend on cross-hairs or arrows which are difficult to find in a digitized image. Once found, the analysis for these locators typically can only use a few pixels to determine a specified point. This limits the accuracy and noise immunity of the desired point.
The present invention proposes to overcome these problems with a target location system that relies on a greater number of points and is accurate and immune to noise.